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Data X:
1418 210907 56 396 3 115 112285 869 120982 56 297 4 109 84786 1530 176508 54 559 12 146 83123 2172 179321 89 967 2 116 101193 901 123185 40 270 1 68 38361 463 52746 25 143 3 101 68504 3201 385534 92 1562 0 96 119182 371 33170 18 109 0 67 22807 1583 149061 44 656 5 100 116174 1439 165446 33 511 0 93 57635 1764 237213 84 655 0 140 66198 1495 173326 88 465 7 166 71701 1373 133131 55 525 7 99 57793 2187 258873 60 885 3 139 80444 1491 180083 66 497 9 130 53855 4041 324799 154 1436 0 181 97668 1706 230964 53 612 4 116 133824 2152 236785 119 865 3 116 101481 1036 135473 41 385 0 88 99645 1882 202925 61 567 7 139 114789 1929 215147 58 639 0 135 99052 2242 344297 75 963 1 108 67654 1220 153935 33 398 5 89 65553 1289 132943 40 410 7 156 97500 2515 174724 92 966 0 129 69112 2147 174415 100 801 0 118 82753 2352 225548 112 892 5 118 85323 1638 223632 73 513 0 125 72654 1222 124817 40 469 0 95 30727 1812 221698 45 683 0 126 77873 1677 210767 60 643 3 135 117478 1579 170266 62 535 4 154 74007 1731 260561 75 625 1 165 90183 807 84853 31 264 4 113 61542 2452 294424 77 992 2 127 101494 1940 215641 46 818 0 121 55813 2662 325107 99 937 0 136 79215 1499 167542 66 507 2 108 55461 865 106408 30 260 1 46 31081 2527 265769 146 927 2 124 83122 2747 269651 67 1269 10 115 70106 1324 149112 56 537 6 128 60578 1383 152871 58 532 5 97 79892 1179 111665 34 345 4 104 49810 2099 116408 61 918 1 59 71570 4308 362301 119 1635 2 125 100708 918 78800 42 330 2 82 33032 1831 183167 66 557 0 149 82875 3373 277965 89 1178 8 149 139077 1713 150629 44 740 3 122 71595 1438 168809 66 452 0 118 72260 496 24188 24 218 0 12 5950 2253 329267 259 764 8 144 115762 744 65029 17 255 5 67 32551 1161 101097 64 454 3 52 31701 2352 218946 41 866 1 108 80670 2144 244052 68 574 5 166 143558 2694 233328 132 825 5 107 120733 1973 256462 105 798 0 127 105195 1769 206161 71 663 12 107 73107 3148 311473 112 1069 8 146 132068 2474 235800 94 921 8 84 149193 2084 177939 82 858 8 141 46821 1954 207176 70 711 8 123 87011 1226 196553 57 503 2 111 95260 1389 174184 53 382 0 98 55183 1496 143246 103 464 5 105 106671 2269 187559 121 717 8 135 73511 1833 187681 62 690 2 107 92945 1268 119016 52 462 5 85 78664 1943 182192 52 657 12 155 70054 893 73566 32 385 6 88 22618 1762 194979 62 577 7 155 74011 1403 167488 45 619 2 104 83737 1425 143756 46 479 0 132 69094 1857 275541 63 817 4 127 93133 1840 243199 75 752 3 108 95536 1502 182999 88 430 6 129 225920 1441 135649 46 451 2 116 62133 1420 152299 53 537 0 122 61370 1416 120221 37 519 1 85 43836 2970 346485 90 1000 0 147 106117 1317 145790 63 637 5 99 38692 1644 193339 78 465 2 87 84651 870 80953 25 437 0 28 56622 1654 122774 45 711 0 90 15986 1054 130585 46 299 5 109 95364 3004 286468 144 1162 1 111 89691 2008 241066 82 714 0 158 67267 2547 148446 91 905 1 141 126846 1885 204713 71 649 1 122 41140 1626 182079 63 512 2 124 102860 1468 140344 53 472 6 93 51715 2445 220516 62 905 1 124 55801 1964 243060 63 786 4 112 111813 1381 162765 32 489 2 108 120293 1369 182613 39 479 3 99 138599 1659 232138 62 617 0 117 161647 2888 265318 117 925 10 199 115929 2845 310839 92 1144 9 91 162901 1982 225060 93 669 7 158 109825 1904 232317 54 707 0 126 129838 1391 144966 144 458 0 122 37510 602 43287 14 214 4 71 43750 1743 155754 61 599 4 75 40652 1559 164709 109 572 0 115 87771 2014 201940 38 897 0 119 85872 2143 235454 73 819 0 124 89275 874 99466 50 273 0 91 192565 1281 100750 72 407 0 119 140867 1401 224549 50 465 4 117 120662 1944 243511 71 603 0 155 101338 391 22938 10 154 0 0 1168 1605 152474 65 577 0 123 65567 530 61857 25 192 4 32 25162 1386 132487 41 411 0 136 40735 2395 317394 86 975 1 117 91413 387 21054 16 146 0 0 855 1742 209641 42 705 5 88 97068 449 31414 19 200 0 25 14116 2699 244749 95 964 2 124 76643 1606 184510 49 537 7 151 110681 1204 128423 64 369 8 145 92696 1138 97839 38 417 2 87 94785 568 38214 34 276 0 27 8773 1459 151101 32 514 2 131 83209 2158 272458 65 822 0 162 93815 1111 172494 52 389 0 165 86687 2833 328107 65 1255 3 159 105547 1955 250579 83 694 0 147 103487 2922 351067 95 1024 3 170 213688 1002 158015 29 400 0 119 71220 956 85439 33 350 0 104 56926 2186 229242 247 719 4 120 91721 3604 351619 139 1277 4 150 115168 1035 84207 29 356 11 112 111194 3261 324598 110 1402 0 136 135777 1587 131069 67 600 4 107 51513 1424 204271 42 480 0 130 74163 1701 165543 65 595 1 115 51633 1249 141722 94 436 0 107 75345 3352 299775 95 1367 9 120 98952 1641 195838 67 564 1 116 102372 2035 173260 63 716 3 79 37238 2312 254488 83 747 10 150 103772 1369 104389 45 467 5 156 123969 2201 199476 70 861 2 118 135400 1900 224330 83 612 1 144 130115 207 14688 10 85 0 0 6023 1645 181633 70 564 2 110 64466 2429 271856 103 824 1 147 54990 151 7199 5 74 0 0 1644 474 46660 20 259 0 15 6179 141 17547 5 69 0 4 3926 872 95227 34 239 0 111 34777 1318 152601 48 438 2 85 73224 1192 101645 63 371 0 44 17140 829 101011 34 238 0 52 27570 186 7176 17 70 0 0 1423 1793 96560 76 503 0 54 22996 2702 175824 107 910 0 80 39992 4691 341570 168 1276 1 80 117105 1112 103597 43 379 1 60 23789 937 112611 41 248 0 78 26706 1290 85574 34 351 0 78 24266 2146 220801 75 720 1 72 44418 1590 92661 61 508 1 45 35232 1590 133328 55 506 0 78 40909 1210 61361 77 451 0 39 13294 2072 125930 75 699 4 68 32387 834 82316 32 245 4 39 21233 1105 102010 53 370 3 50 44332 1272 101523 42 316 0 88 61056 761 41566 35 229 5 36 13497 1988 99923 66 617 0 99 32334 620 22648 19 184 0 39 44339 800 46698 45 274 0 52 10288 1684 131698 65 502 0 75 65622 1050 91735 35 382 0 71 16563 1502 79863 37 438 1 71 29011 1421 108043 62 466 1 54 34553 1060 98866 18 397 0 49 23517 1417 120445 118 457 0 59 51009 946 116048 64 230 0 75 33416 1926 250047 81 651 0 71 83305 1577 136084 30 671 0 51 27142 961 92499 32 319 0 71 21399 1254 135781 31 433 2 47 24874 1335 74408 67 434 4 28 34988 1597 81240 66 503 0 68 45549 1639 133368 36 535 1 64 32755 1018 98146 40 459 0 68 27114 1383 79619 43 426 3 40 20760 1314 59194 31 288 6 80 37636 1335 139942 42 498 0 88 65461 1403 118612 46 454 2 48 30080 910 72880 33 376 0 76 24094
Names of X columns:
pageviews time_in_rfc logins compendium_views_info shared_compendiums feedback_messages_p1 totsize
Endogenous Variable (Column Number)
Categorization
none
none
quantiles
hclust
equal
Number of categories (only if categorization<>none)
Cross-Validation? (only if categorization<>none)
no
no
yes
Chart options
R Code
library(party) library(Hmisc) par1 <- as.numeric(par1) par3 <- as.numeric(par3) x <- data.frame(t(y)) is.data.frame(x) x <- x[!is.na(x[,par1]),] k <- length(x[1,]) n <- length(x[,1]) colnames(x)[par1] x[,par1] if (par2 == 'kmeans') { cl <- kmeans(x[,par1], par3) print(cl) clm <- matrix(cbind(cl$centers,1:par3),ncol=2) clm <- clm[sort.list(clm[,1]),] for (i in 1:par3) { cl$cluster[cl$cluster==clm[i,2]] <- paste('C',i,sep='') } cl$cluster <- as.factor(cl$cluster) print(cl$cluster) x[,par1] <- cl$cluster } if (par2 == 'quantiles') { x[,par1] <- cut2(x[,par1],g=par3) } if (par2 == 'hclust') { hc <- hclust(dist(x[,par1])^2, 'cen') print(hc) memb <- cutree(hc, k = par3) dum <- c(mean(x[memb==1,par1])) for (i in 2:par3) { dum <- c(dum, mean(x[memb==i,par1])) } hcm <- matrix(cbind(dum,1:par3),ncol=2) hcm <- hcm[sort.list(hcm[,1]),] for (i in 1:par3) { memb[memb==hcm[i,2]] <- paste('C',i,sep='') } memb <- as.factor(memb) print(memb) x[,par1] <- memb } if (par2=='equal') { ed <- cut(as.numeric(x[,par1]),par3,labels=paste('C',1:par3,sep='')) x[,par1] <- as.factor(ed) } table(x[,par1]) colnames(x) colnames(x)[par1] x[,par1] if (par2 == 'none') { m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x) } load(file='createtable') if (par2 != 'none') { m <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data = x) if (par4=='yes') { a<-table.start() a<-table.row.start(a) a<-table.element(a,'10-Fold Cross Validation',3+2*par3,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'',1,TRUE) a<-table.element(a,'Prediction (training)',par3+1,TRUE) a<-table.element(a,'Prediction (testing)',par3+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Actual',1,TRUE) for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE) a<-table.element(a,'CV',1,TRUE) for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE) a<-table.element(a,'CV',1,TRUE) a<-table.row.end(a) for (i in 1:10) { ind <- sample(2, nrow(x), replace=T, prob=c(0.9,0.1)) m.ct <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data =x[ind==1,]) if (i==1) { m.ct.i.pred <- predict(m.ct, newdata=x[ind==1,]) m.ct.i.actu <- x[ind==1,par1] m.ct.x.pred <- predict(m.ct, newdata=x[ind==2,]) m.ct.x.actu <- x[ind==2,par1] } else { m.ct.i.pred <- c(m.ct.i.pred,predict(m.ct, newdata=x[ind==1,])) m.ct.i.actu <- c(m.ct.i.actu,x[ind==1,par1]) m.ct.x.pred <- c(m.ct.x.pred,predict(m.ct, newdata=x[ind==2,])) m.ct.x.actu <- c(m.ct.x.actu,x[ind==2,par1]) } } print(m.ct.i.tab <- table(m.ct.i.actu,m.ct.i.pred)) numer <- 0 for (i in 1:par3) { print(m.ct.i.tab[i,i] / sum(m.ct.i.tab[i,])) numer <- numer + m.ct.i.tab[i,i] } print(m.ct.i.cp <- numer / sum(m.ct.i.tab)) print(m.ct.x.tab <- table(m.ct.x.actu,m.ct.x.pred)) numer <- 0 for (i in 1:par3) { print(m.ct.x.tab[i,i] / sum(m.ct.x.tab[i,])) numer <- numer + m.ct.x.tab[i,i] } print(m.ct.x.cp <- numer / sum(m.ct.x.tab)) for (i in 1:par3) { a<-table.row.start(a) a<-table.element(a,paste('C',i,sep=''),1,TRUE) for (jjj in 1:par3) a<-table.element(a,m.ct.i.tab[i,jjj]) a<-table.element(a,round(m.ct.i.tab[i,i]/sum(m.ct.i.tab[i,]),4)) for (jjj in 1:par3) a<-table.element(a,m.ct.x.tab[i,jjj]) a<-table.element(a,round(m.ct.x.tab[i,i]/sum(m.ct.x.tab[i,]),4)) a<-table.row.end(a) } a<-table.row.start(a) a<-table.element(a,'Overall',1,TRUE) for (jjj in 1:par3) a<-table.element(a,'-') a<-table.element(a,round(m.ct.i.cp,4)) for (jjj in 1:par3) a<-table.element(a,'-') a<-table.element(a,round(m.ct.x.cp,4)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') } } m bitmap(file='test1.png') plot(m) dev.off() bitmap(file='test1a.png') plot(x[,par1] ~ as.factor(where(m)),main='Response by Terminal Node',xlab='Terminal Node',ylab='Response') dev.off() if (par2 == 'none') { forec <- predict(m) result <- as.data.frame(cbind(x[,par1],forec,x[,par1]-forec)) colnames(result) <- c('Actuals','Forecasts','Residuals') print(result) } if (par2 != 'none') { print(cbind(as.factor(x[,par1]),predict(m))) myt <- table(as.factor(x[,par1]),predict(m)) print(myt) } bitmap(file='test2.png') if(par2=='none') { op <- par(mfrow=c(2,2)) plot(density(result$Actuals),main='Kernel Density Plot of Actuals') plot(density(result$Residuals),main='Kernel Density Plot of Residuals') plot(result$Forecasts,result$Actuals,main='Actuals versus Predictions',xlab='Predictions',ylab='Actuals') plot(density(result$Forecasts),main='Kernel Density Plot of Predictions') par(op) } if(par2!='none') { plot(myt,main='Confusion Matrix',xlab='Actual',ylab='Predicted') } dev.off() if (par2 == 'none') { detcoef <- cor(result$Forecasts,result$Actuals) a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goodness of Fit',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Correlation',1,TRUE) a<-table.element(a,round(detcoef,4)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'R-squared',1,TRUE) a<-table.element(a,round(detcoef*detcoef,4)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'RMSE',1,TRUE) a<-table.element(a,round(sqrt(mean((result$Residuals)^2)),4)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'#',header=TRUE) a<-table.element(a,'Actuals',header=TRUE) a<-table.element(a,'Forecasts',header=TRUE) a<-table.element(a,'Residuals',header=TRUE) a<-table.row.end(a) for (i in 1:length(result$Actuals)) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) a<-table.element(a,result$Actuals[i]) a<-table.element(a,result$Forecasts[i]) a<-table.element(a,result$Residuals[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable.tab') } if (par2 != 'none') { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Confusion Matrix (predicted in columns / actuals in rows)',par3+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'',1,TRUE) for (i in 1:par3) { a<-table.element(a,paste('C',i,sep=''),1,TRUE) } a<-table.row.end(a) for (i in 1:par3) { a<-table.row.start(a) a<-table.element(a,paste('C',i,sep=''),1,TRUE) for (j in 1:par3) { a<-table.element(a,myt[i,j]) } a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') }
Compute
Summary of computational transaction
Raw Input
view raw input (R code)
Raw Output
view raw output of R engine
Computing time
2 seconds
R Server
Big Analytics Cloud Computing Center
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